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Hip geometry (HG) is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: 1) Femoral neck length; 2) Neck-shaft angle; 3) Femoral neck width, and 4) Femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16-93 years. Association analyses were performed with ~ 2.5 million polymorphismsunder an additive model adjusted for age and body mass index; an additional analysis also adjusted for height. Replication analyses of meta-GWAS significant loci (at genome-wide significance, GWS, threshold p≤5x10-8) were performed in 7 additional cohorts in-silico. In meta-analysis not adjusting for height (combined Discovery and Replication stages), GWS associations were found on chr. 4 (in HHIP), chr. 8 (C8orf34), chr. 13 (FAM10A4 and DLEU2), and chr. 20 (inDDX27). The height-adjusted meta-analysis showed significant associations at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several HG signals overlapped with bone mineral density (BMD), including JAG1 on chr. 20, near TNFRSF11B (chr. 8), SOX6 and LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 x 10-5). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p=0.0007), and rs6556301 (intergenic, chr.5) and PDLIM7 expression(p=0.005). In conclusion, we found associations between HG measures and several genes being part of biological pathways relevant to BMD and fractures. The results provide a defined set of genes facilitating further experimental exploration and validation to understand biological mechanisms underlying human bone geometry and etiology of bone fragility.

Objective: A major barrier to genetic studies of OA is the need to obtain large numbers of individuals with standardized radiographic evaluations for OA. To address this gap, we performed a genome-wide association study (GWAS) of radiographically-defined tibiofemoral knee OA in 3,898 cases and 3,168 controls from four well-characterized North American cohorts, and replication analysis of published OA loci. Methods: We performed meta-analysis using a two-stage design. Stage 1 (discovery) consisted of a GWAS meta-analysis of radiographic knee OA carried out in the Osteoarthritis Initiative and the Johnston County Osteoarthritis Project. Knee OA was defined as definitive osteophytes and possible joint space narrowing or total joint replacement in one or both knees. Stage 2 (validation) was performed in the Multicenter Osteoarthritis Study and Genetics of Osteoarthritis Study. We genotyped lead meta-analysis variants (P-value

Genetic variation in the platelet endothelial aggregation receptor 1 (PEAR1) gene, most notably rs12041331, is implicated in altered on-aspirin platelet aggregation and increased cardiovascular event risk. We prospectively tested the effects of aspirin administration at commonly prescribed doses (81, 162, and 324 mg/day) on agonist-induced platelet aggregation by rs12041331 genotype in 67 healthy individuals. Prior to aspirin administration, rs12041331 minor allele carriers had significantly reduced adenosine diphosphate (ADP)-induced platelet aggregation compared with noncarriers (P = 0.03) but was not associated with other platelet pathways. In contrast, rs12041331 was significantly associated with on-aspirin platelet aggregation when collagen and epinephrine were used to stimulate platelet aggregation (P < 0.05 for all associations), but not ADP. The influence of PEAR1 rs12041331 on platelet aggregation is pathway-specific and is altered by aspirin at therapeutic doses, but not in a dose-dependent manner. Additional studies are needed to determine the impact of PEAR1 on cardiovascular events in aspirin-treated patients.

Osteoarthritis (OA) risk is widely recognized to be heritable but few loci have been identified. Observational studies have identified higher systemic bone mineral density (BMD) to be associated with an increased risk of radiographic knee osteoarthritis. With this in mind, we sought to evaluate whether well-established genetic loci for variance in BMD are associated with risk for radiographic OA in the Osteoarthritis Initiative (OAI) and the Johnston County Osteoarthritis (JoCo) Project. Cases had at least one knee with definite radiographic OA, defined as the presence of definite osteophytes with or without joint space narrowing (Kellgren-Lawrence [KL] grade ≥ 2) and controls were absent for definite radiographic OA in both knees (KL grade ≤ 1 bilaterally). There were 2014 and 658 Caucasian cases, respectively, in the OAI and JoCo Studies, and 953 and 823 controls. Single nucleotide polymorphisms (SNPs) were identified for association analysis from the literature. Genotyping was carried out on Illumina 2.5M and 1M arrays in Genetic Components of Knee OA (GeCKO) and JoCo, respectively and imputation was done. Association analyses were carried out separately in each cohort with adjustments for age, body mass index (BMI), and sex, and then parameter estimates were combined across the two cohorts by meta-analysis. We identified four SNPs significantly associated with prevalent radiographic knee OA. The strongest signal (p = 0.0009; OR = 1.22; 95% CI, 1.08–1.37) maps to 12q3, which contains a gene coding for SP7. Additional loci map to 7p14.1 (TXNDC3), 11q13.2 (LRP5), and 11p14.1 (LIN7C). For all four loci the allele associated with higher BMD was associated with higher odds of OA. A BMD risk allele score was not significantly associated with OA risk. This meta-analysis demonstrates that several genomewide association studies (GWAS)-identified BMD SNPs are nominally associated with prevalent radiographic knee OA and further supports the hypothesis that BMD, or its determinants, may be a risk factor contributing to OA development. © 2014 American Society for Bone and Mineral Research.

Carriers of two copies of the loss-of-function CYP2C19*2 variant convert less clopidogrel into its active metabolite, resulting in diminished antiplatelet responses and higher cardiovascular event rates. To evaluate whether increasing the daily clopidogrel dose in poor metabolizers (PM) overcomes the effect of the CYP2C19 * 2 variant, we enrolled 18 healthy participants in a genotype-stratified, multi-dose, three-period, fixed-sequence crossover study. Six participants with the *1/*1 extensive (EM), *1/*2 intermediate (IM), and *2/*2 poor metabolizer genotypes each received 75 mg, 150 mg, and 300 mg each for 8 days. In each period, maximal platelet aggregation 4 hours post-dose (MPA4) and active metabolite area under the curve (AUC) differed among genotype groups (P

International audience; While aspirin is generally effective for prevention of cardiovascular disease, considerable variation in drug response exists, resulting in some individuals displaying high on-treatment platelet reactivity. We used pharmacometabolomics to define pathways implicated in variation of response to treatment. We profiled serum samples from healthy subjects pre- and postaspirin (14 days, 81 mg/day) using mass spectrometry. We established a strong signature of aspirin exposure independent of response (15/34 metabolites changed). In our discovery (N = 80) and replication (N = 125) cohorts, higher serotonin levels pre- and postaspirin correlated with high, postaspirin, collagen-induced platelet aggregation. In a third cohort, platelets from subjects with the highest levels of serotonin preaspirin retained higher reactivity after incubation with aspirin than platelets from subjects with the lowest serotonin levels preaspirin (72 ± 8 vs. 61 ± 11%, P = 0.02, N = 20). Finally, ex vivo, serotonin strongly increased platelet reactivity after platelet incubation with aspirin (+20%, P = 4.9 × 10−4, N = 12). These results suggest that serotonin is implicated in aspirin response variability.